
"""simple docstring"""






import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)




def __magic_name__	(				_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		List[str]=False			)					->						Any:

	'''simple docstring'''




	lowerCAmelCase												=  []
	for i in range(config.num_hidden_layers			):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F"""module.blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")			)
		rename_keys.append((F"""module.blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")			)
		rename_keys.append(
		    (F"""module.blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")			)
		rename_keys.append((F"""module.blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")			)
		rename_keys.append((F"""module.blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")			)
		rename_keys.append((F"""module.blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")			)
		rename_keys.append((F"""module.blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")			)
		rename_keys.append((F"""module.blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")			)
		rename_keys.append((F"""module.blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")			)
		rename_keys.append((F"""module.blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")			)

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        ('''module.cls_token''', '''vit.embeddings.cls_token'''),
	        ('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
	        ('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
	        ('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
	    ]			)

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('''module.norm.weight''', '''layernorm.weight'''),
		        ('''module.norm.bias''', '''layernorm.bias'''),
		    ]			)

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		lowerCAmelCase												=  [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''			) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('''norm.weight''', '''vit.layernorm.weight'''),
		        ('''norm.bias''', '''vit.layernorm.bias'''),
		        ('''head.weight''', '''classifier.weight'''),
		        ('''head.bias''', '''classifier.bias'''),
		    ]			)

	return rename_keys




def __magic_name__	(				_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		Optional[int],					_lowerCamelCase:		Union[str, Any]=False			)					->						str:

	'''simple docstring'''




	for i in range(config.num_hidden_layers			):
		if base_model:
			lowerCAmelCase												=  ''''''
		else:
			lowerCAmelCase												=  '''vit.'''
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		lowerCAmelCase												=  state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight"""			)
		lowerCAmelCase												=  state_dict.pop(F"""module.blocks.{i}.attn.qkv.bias"""			)
		# next, add query, keys and values (in that order) to the state dict
		lowerCAmelCase												=  in_proj_weight[
		    : config.hidden_size, :
		]
		lowerCAmelCase												=  in_proj_bias[: config.hidden_size]
		lowerCAmelCase												=  in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowerCAmelCase												=  in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		lowerCAmelCase												=  in_proj_weight[
		    -config.hidden_size :, :
		]
		lowerCAmelCase												=  in_proj_bias[-config.hidden_size :]




def __magic_name__	(				_lowerCamelCase:		Any			)					->						Union[str, Any]:

	'''simple docstring'''




	lowerCAmelCase												=  ['''head.weight''', '''head.bias''']
	for k in ignore_keys:
		state_dict.pop(_lowerCamelCase,					_lowerCamelCase			)




def __magic_name__	(				_lowerCamelCase:		Optional[int]			)					->						int:

	'''simple docstring'''




	lowerCAmelCase												=  [
	    '''module.fc.fc1.weight''',
	    '''module.fc.fc1.bias''',
	    '''module.fc.bn1.weight''',
	    '''module.fc.bn1.bias''',
	    '''module.fc.bn1.running_mean''',
	    '''module.fc.bn1.running_var''',
	    '''module.fc.bn1.num_batches_tracked''',
	    '''module.fc.fc2.weight''',
	    '''module.fc.fc2.bias''',
	    '''module.fc.bn2.weight''',
	    '''module.fc.bn2.bias''',
	    '''module.fc.bn2.running_mean''',
	    '''module.fc.bn2.running_var''',
	    '''module.fc.bn2.num_batches_tracked''',
	    '''module.fc.fc3.weight''',
	    '''module.fc.fc3.bias''',
	]
	for k in ignore_keys:
		state_dict.pop(_lowerCamelCase,					_lowerCamelCase			)




def __magic_name__	(				_lowerCamelCase:		str,					_lowerCamelCase:		List[str],					_lowerCamelCase:		int			)					->						str:

	'''simple docstring'''




	lowerCAmelCase												=  dct.pop(_lowerCamelCase			)
	lowerCAmelCase												=  val




def __magic_name__	(				_lowerCamelCase:		int,					_lowerCamelCase:		Optional[Any]			)					->						Union[str, Any]:

	'''simple docstring'''




	lowerCAmelCase												=  ViTMSNConfig()
	lowerCAmelCase												=  1_000

	lowerCAmelCase												=  '''datasets/huggingface/label-files'''
	lowerCAmelCase												=  '''imagenet-1k-id2label.json'''
	lowerCAmelCase												=  json.load(open(hf_hub_download(_lowerCamelCase,					_lowerCamelCase			),					'''r'''			)			)
	lowerCAmelCase												=  {int(_lowerCamelCase			): v for k, v in idalabel.items()}
	lowerCAmelCase												=  idalabel
	lowerCAmelCase												=  {v: k for k, v in idalabel.items()}

	if "s16" in checkpoint_url:
		lowerCAmelCase												=  384
		lowerCAmelCase												=  1_536
		lowerCAmelCase												=  6
	elif "l16" in checkpoint_url:
		lowerCAmelCase												=  1_024
		lowerCAmelCase												=  4_096
		lowerCAmelCase												=  24
		lowerCAmelCase												=  16
		lowerCAmelCase												=  0.1
	elif "b4" in checkpoint_url:
		lowerCAmelCase												=  4
	elif "l7" in checkpoint_url:
		lowerCAmelCase												=  7
		lowerCAmelCase												=  1_024
		lowerCAmelCase												=  4_096
		lowerCAmelCase												=  24
		lowerCAmelCase												=  16
		lowerCAmelCase												=  0.1

	lowerCAmelCase												=  ViTMSNModel(_lowerCamelCase			)

	lowerCAmelCase												=  torch.hub.load_state_dict_from_url(_lowerCamelCase,					map_location='''cpu'''			)['''target_encoder''']

	lowerCAmelCase												=  ViTImageProcessor(size=config.image_size			)

	remove_projection_head(_lowerCamelCase			)
	lowerCAmelCase												=  create_rename_keys(_lowerCamelCase,					base_model=_lowerCamelCase			)

	for src, dest in rename_keys:
		rename_key(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)
	read_in_q_k_v(_lowerCamelCase,					_lowerCamelCase,					base_model=_lowerCamelCase			)

	model.load_state_dict(_lowerCamelCase			)
	model.eval()

	lowerCAmelCase												=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''

	lowerCAmelCase												=  Image.open(requests.get(_lowerCamelCase,					stream=_lowerCamelCase			).raw			)
	lowerCAmelCase												=  ViTImageProcessor(
	    size=config.image_size,					image_mean=_lowerCamelCase,					image_std=_lowerCamelCase			)
	lowerCAmelCase												=  image_processor(images=_lowerCamelCase,					return_tensors='''pt'''			)

	# forward pass
	torch.manual_seed(2			)
	lowerCAmelCase												=  model(**_lowerCamelCase			)
	lowerCAmelCase												=  outputs.last_hidden_state

	# The following Colab Notebook was used to generate these outputs:
	# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
	if "s16" in checkpoint_url:
		lowerCAmelCase												=  torch.tensor([[-1.0915, -1.4876, -1.1809]]			)
	elif "b16" in checkpoint_url:
		lowerCAmelCase												=  torch.tensor([[14.2889, -18.9045, 11.7281]]			)
	elif "l16" in checkpoint_url:
		lowerCAmelCase												=  torch.tensor([[41.5028, -22.8681, 45.6475]]			)
	elif "b4" in checkpoint_url:
		lowerCAmelCase												=  torch.tensor([[-4.3868, 5.2932, -0.4137]]			)
	else:
		lowerCAmelCase												=  torch.tensor([[-0.1792, -0.6465, 2.4263]]			)

	# verify logits
	assert torch.allclose(last_hidden_state[:, 0, :3],					_lowerCamelCase,					atol=1E-4			)

	print(F"""Saving model to {pytorch_dump_folder_path}"""			)
	model.save_pretrained(_lowerCamelCase			)

	print(F"""Saving image processor to {pytorch_dump_folder_path}"""			)
	image_processor.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
					    type=str,
					    help="""URL of the checkpoint you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)

					UpperCAmelCase        =				parser.parse_args()
					convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''BlipImageProcessor'''
			lowercase        =     ('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  False
				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.image_processor






			def __call__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	ImageInput = None      ,SCREAMING_SNAKE_CASE_  :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, PaddingStrategy] = False      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, TruncationStrategy] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	int = 0      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[bool] = None      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, TensorType]] = None      ,**SCREAMING_SNAKE_CASE_  :	str      ,)       ->	BatchEncoding:





				"""simple docstring"""


				if images is None and text is None:
					raise ValueError('''You have to specify either images or text.'''			)

				# Get only text
				if images is None:
					lowerCAmelCase												=  self.tokenizer
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
					return text_encoding

				# add pixel_values
				lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
				else:
					lowerCAmelCase												=  None

				if text_encoding is not None:
					encoding_image_processor.update(SCREAMING_SNAKE_CASE_			)

				return encoding_image_processor






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


